Underwater Source Localization Using an Artificial Lateral Line System With Pressure and Flow Velocity Sensor Fusion

Jiang, Yonggang; Gong, Zheng; Yang, Zhen; Ma, Zhiqiang; Wang, Chunxuan; Wang, Yanjie; Zhang, Deyuan

doi:10.1109/tmech.2021.3062869

Cited by 38 publications

(24 citation statements)

References 39 publications

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“…The system has a broad prospect in underwater detection and exploration. In 2022, Jiang et al [29] studied a multi-mode sideline system based on IPMC dual sensors. The dual sensors of the system, consisting of a pressure sensor and a flow sensor, have a better ability to locate vibration dipoles than a single sensor.…”

Section: Flow Sensormentioning

confidence: 99%

Ionic polymer metal composite (IPMC): an overview and its sensing applications

Zhao

Tian²,

Li³

et al. 2023

International Conference on Optoelectronic Materials and Devices (ICOMD 2022)

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In recent years, ionic polymer metal composites (IPMCs), as one important class of electroactive polymers (EAPs), have received tremendous interest for their potential applications in various sensing scenarios due to their softness, high sensitivity, inherent polarity, built-in actuation, and sensing capabilities, ability to work in water, and direct mechanosensory property. They also have bio-compatibility and amenability to microfabrication. This paper reviews the characteristics, sensing mechanisms, and typical sensing applications of IPMCs to provide references for interested researchers in related fields. The influences of different factors on IPMC sensor performance are also discussed.

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Section: Flow Sensormentioning

confidence: 99%

Ionic polymer metal composite (IPMC): an overview and its sensing applications

Zhao

Tian²,

Li³

et al. 2023

International Conference on Optoelectronic Materials and Devices (ICOMD 2022)

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“…MLP is a feedforward neural network, which has a wide range of applications in modeling nonlinear data [17]. To simulate the fish lateral line system, we use MLP model the functional relationship between pressure and attitude.…”

Section: Multi-layer Perceptron Modelmentioning

confidence: 99%

Multiple Attitude Estimation Models based on a Pressure Sensor Array

Wang¹,

Yu²

2023

J. Phys.: Conf. Ser.

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Autonomous navigation is critical for autonomous underwater vehicle to complete underwater tasks, and attitude is an essential parameter of autonomous navigation. Inspired by the biomimicry studies, three attitude estimation models based on a pressure sensor array are constructed, which are the direct method, the multi-layer perception model and the attitude estimation model based on physical knowledge respectively. The field tests are carried out to verify the effectiveness of the proposed methods, and the advantages and disadvantages of the three methods are compared. It turns out that the attitude estimation model based on physical knowledge achieves the optimal results, and the estimated error of pitch is lower than 1.9133°, and the estimated error of roll is lower than 2.6719°. Further, there is no cumulative attitude error, which complies that the attitude estimation model based on physical knowledge has a high potential for applications in autonomous navigation systems.

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“…Artificial sideline arrays developed using commercial pressure sensors for dipole source detection have also received extensive attention and intensive research [39][40][41]. For example, Jiang et al developed a sideline array integrating IPMC velocity modal and pressure sensors to achieve multimodal sensing fusion and reduce localization errors [39].…”

Section: Design Of Artificial Lateral Line Arraymentioning

confidence: 99%

“…Liu et al used artificial neural networks (ANNs) to identify the coordinates, frequency, and amplitude of vibration sources and achieved an accuracy of 93% [45]. Jiang et al achieved dipole source localization in 2D and 3D using MLP networks [39]. Wolf et al used the long short-term memory (LSTM) model to process pressure-varying time series data.…”

Section: Neural Network Approach For Underwater Localizationmentioning

confidence: 99%

Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method

Hang

Wang

et al. 2022

Applied Sciences

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The lateral line system is an essential mechanosensory organ for organisms such as fish; it perceives the fluid environment in the near-field through the neuromasts on the lateral line system, supporting behaviors (e.g., obstacle avoidance and predation in fish). Inspired by the near-field perception ability of fish, we propose an artificial lateral line system composed of pressure sensors that respond to a target’s relative position by measuring the pressure change of the target vibration near the lateral line. Based on the shortcomings of the idealized constrained modeling approach, a multilayer perceptron network was built in this paper to process the pressure signal and predict the coordinates on a two-dimensional plane. Previous studies primarily focused on the localization of a single dipole source and rarely considered the localization of multiple vibration sources. In this paper, we explore the localization of numerous dipole sources of the same and different frequency vibrations based on the prediction of the two-dimensional coordinates of double dipoles. The experimental results show that the mutual interference of two vibration sources causes an increase in the localization error. Compared with multiple sources of vibration at the same frequency, the positioning accuracies of various vibration sources at different frequencies are higher. In addition, we explored the effects of the number of sensors on the localization results.

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Underwater Source Localization Using an Artificial Lateral Line System With Pressure and Flow Velocity Sensor Fusion

Cited by 38 publications

References 39 publications

Ionic polymer metal composite (IPMC): an overview and its sensing applications

Ionic polymer metal composite (IPMC): an overview and its sensing applications

Multiple Attitude Estimation Models based on a Pressure Sensor Array

Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method

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